Investigating and Modeling the significant reasons of Percutaneous Coronary Intervention patients to participate rarely in cardiac rehabilitation - A data mining approach

Document Type : Articles

Authors

1 Faculty of Industrial and Systems Engineering (IT Engineering Group), Tarbiat Modares University, Tehran, Iran.

2 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

3 Department of Cardiology, Tehran University of Medical Sciences, Tehran, Iran.

4 Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran.

Abstract

Objective: The high prevalence of cardiovascular diseases has caused many health problems in countries. Cardiac Rehabilitation Programs (CRPs) is a complementary therapy for Percutaneous Coronary Intervention (PCI) patients. However, PCI patients hardly attend CRPs. This study aims to decipher the reasons why PCI patients rarely participate in CRPs after PCI.Methods: The parameters affecting the attendance of the patients at CRPs were identified by using the previous studies and opinions of experts. A questionnaire was designed based on the identified parameters and distributed among PCI patients who were referred to Tehran Heart Center Hospital.Results: According to data mining approach, 184 samples were collected and classified with three algorithms (Decision Trees, k-Nearest Neighbor (kNN), and Naïve Bayes). The obtained results by decision trees were superior with the average accuracy of 82%, while kNN and Naïve Bayes obtained 81.2% and 78%, respectively. Results showed that lack of physician’s advice was the most significant reason for non-participation of PCI patients in CRPs (P< .0001). Other factors were family and friends’ encouragement, paying expenses by insurance, awareness of the benefits of the CRPs, and comorbidity, respectively.Conclusion: Results of the best model can enhance the quality of services, promote health and prevent additional costs for patients. Keywords: Cardiovascular Disease, Percutaneous Coronary Intervention, Cardiac Rehabilitation Programs, Data Mining, Classification

References

  1. Yu RX and Muller-Riemenschneider F. Effectiveness of exercise after PCI in the secondary prevention of coronary heart disease: A systematic review. Eur J Integr Med 2011; 3: e63-e69.
  2. Iran: What causes the most deaths? What causes the most premature death?
  3. http://www.healthdata.org/iran (2016).
  4. Lavie CJ and Milani RV. Cardiac Rehabilitation and Exercise Training in Secondary Coronary Heart Disease Prevention. Prog Cardiovasc Dis 2011; 53: 397–403.
  5. Magalhaes S, Ribeiro MM, Barreira A, et al. Long-term effects of a cardiac rehabilitation program in the control of cardiovascular risk factors. Rev Port Cardiol 2013; 32: 191-199.
  6. Cui F, Ren Y, Jin H, et al. Rehabilitation training improves exercise tolerance after
  7. percutaneous coronary intervention. J Biomed Res 2012; 26: 248-252.
  8. Giannuzzi P, Saner H, Bjornstad H, et al. Secondary Prevention Through Cardiac Rehabilitation Position Paper of the Working Group on Cardiac Rehabilitation and Exercise Physiology of the European Society of Cardiology. Eur Heart J 2003; 24: 1273-1278.
  9. Menezes AR, Lavie CJ, Milani RV, et al. Cardiac Rehabilitation in the United States. Prog Cardiovasc Dis 2014; 56: 522–529.
  10. Blumenthal JA, Sherwood A, Smith PJ, Watkins L, Mabe S, Kraus WE, Ingle K, Miller P & Hinderliter A. Enhancing Cardiac Rehabilitation with Stress Management Training. Circulation 2016; 133: 1341-1350.
  11. Herber OR, Smith K, White M, & Jones MC. ‘Just not for me’ – contributing factors to nonattendance/noncompletion at phase III cardiac rehabilitation in acute coronary syndrome patients: a qualitative enquiry. J Clin Nurs 2017; DOI: 10.1111/jocn.13722.
  12. Aragam KG, Moscucci M, Smith DE, et al. Trends and disparities in referral to cardiac rehabilitation after percutaneous coronary intervention. Am Heart J 2011; 161: 544-551.
  13. Thomas RJ, King M, Lui K, et al. AACVPR/ACC/AHA 2007 Performance Measures on Cardiac Rehabilitation for Referral to and Delivery of Cardiac Rehabilitation/Secondary Prevention Services. J Am Coll Cardiol 2007; 50: 1400-1433.
  14. Percutaneous coronary intervention (PCI or angioplasty with stent). http://www.heartandstroke.com/site/c.ikIQLcMWJtE/b.3831925/k.4F32/Heart_disease__Percutaneous_coronary_intervention_PCI_or_angioplasty_with_stent.htm (2007, accessed November 2013).
  15. Levine GN, Bates ER, Blankenship JC, et al. 2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention. J Am Coll Cardiol 2011; 58: e44-e122.
  16. Galve E, Castero A, Cordero A, et al. Update in Cardiology: Vascular Risk and Cardiac Rehabilitation. Rev Esp Cardiol 2013; 66: 124-130.
  17. Banerjee AT, Grace SL, Thomas SG, et al. Cultural factors facilitating cardiac rehabilitation participation among Canadian South Asians: A qualitative study. Heart & Lung 2010; 39: 494–503.
  18. Clark AM, King-Shier KM, Thompson DR, et al. A qualitative systematic review of influences on attendance at cardiac rehabilitation programs after referral. Am Heart J 2012; 164: 835-845.
  19. Dunlay SM, Witt BJ, Allison TG, et al. Barriers to participation in cardiac rehabilitation. Am Heart J 2009; 158: 852-859.
  20. Lieberman L, Meana M and Stewart DE. Cardiac rehabilitation: Gender differences in factors influencing participation. J Women’s Health 1998; 7: 717-723.
  21. Brown TM, Hernandez AF, Bittner V, et al. Predictors of Cardiac Rehabilitation Referral in Coronary Artery Disease Patients. J Am Coll Cardiol 2009; 54: 515-521.
  22. Gaalema DE, Cutler AY, Higgins ST, et al. Smoking and Cardiac Rehabilitation Participation: Associations with Referral, Attendance and Adherence. Prev Med 2015; 80: 67-74.
  23. Gaalema DE, Savage PD, Rengo JL, et al. Patient Characteristics Predictive of Cardiac Rehabilitation Adherence. J Cardiopulm Rehabil Prev 2017; 37(2): 103-110.
  24. Hand D, Mannila H and Smyth P. Principles of Data Mining. Cambridge, Massachusetts: The MIT Press, 2001.
  25. Yoo I, Alafaireet P, Marinov M, Pena-Hernandez K, Gopidi R, Chang J-F & Hua L. Data Mining in Healthcare and Biomedicine: A Survey of the Literature. J Med Syst 2012; 36: 2431-2448.
  26. Lin WT, Wu YC, Zheng JS, et al. Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital. Expert Syst Appl 2011; 38: 11078–11084.
  27. Hu PJH, Wei CP, Cheng TH, et al. Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making. Decis Support Syst 2007; 43: 1226-1241.
  28. Jerez-Aragones JM, Gomez-Ruiz JA, Ramos-Jimenez G, et al. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med 2003; 27: 45-63.
  29. Harper PR and Winslett DJ. Classification trees: A possible method for maternity risk grouping. Eur J Oper Res 2006; 169: 146-156.
  30. Fayyad U, Piatetsky-Shapiro G and Smyth P. From Data Mining to Knowledge Discovery in Databases. AI Magazine 1996; 17: 37-54.
  31. Feng Y, Wu Z, Zhou X, et al. Knowledge discovery in traditional Chinese medicine: State of the art and perspectives. Artif Intell Med 2006; 38: 219-236.
  32. Ltifi H, Ben Mohamed E and ben Ayed M. Interactive visual knowledge discovery
  33. from data-based temporal decision support system. Info Vis 2016; 15: 31-50.
  34. King KM, Humen DP, Smith HL, et al. Predicting and explaining cardiac rehabilitation attendance. Can J Cardiol 2001; 17: 291-296.
  35. Clark AM, Sharp C and Macintyre PD. The role of age in moderating access to cardiac rehabilitation in Scotland. Ageing and Society 2002; 22: 501-515.
  36. Leung YW, Brual J, Macpherson A, et al. Geographic issues in cardiac rehabilitation utilization: A narrative review. Health & Place 2010; 16: 1196-1205.
  37. Tod AM, Lacey EA and McNeill F. 'I'm still waiting...': barriers to accessing cardiac rehabilitation services. J Adv Nurs 2002; 40: 421-431.
  38. Grace SL, Abbey SE, Shnek ZM, et al. Cardiac rehabilitation I: review of psychosocial factors. Gen Hosp Psychiat 2002; 24: 121-126.
  39. Han J, Kamber M and Pei J. Data Mining Concepts and Techniques. 3rd ed. MA, USA: Elsevier, 2012.
  40. Marcano-Cedeño A, Chausa P, Garcia A, et al. Data mining applied to the cognitive rehabilitation of patients with acquired brain injury. Expert Syst Appl 2013; 40: 1054-1060.
  41. Targo L. Data Mining with R. Learning with Case Studies. Florida: Taylor and Francis Group, 2011.
  42. Shilaskar S & Ghatol A. Feature selection for medical diagnosis: Evaluation for cardiovascular diseases. Expert Syst Appl 2013; 40: 4146-4153.
  43. Fan CY, Chang PC, Lin JJ, et al. A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl Soft Comput 2011; 11: 632-644.
  44. Sperandei S. Understanding logistic regression analysis. Biochemia Medica 2014; 24: 12-18.
  45. Bard Y. Nonlinear Parameter Estimation. Academic Press, 1974, University of Minnesota: 2010.
  46. Ghalghamash R, Goosheh B, Keyhani M, Bazrafshan AR, Barzegari M & Hosseni A. Importance of phase ² of cardiac rehabilitation. J Med Counc Islam Repub Iran 2006; 24: 123–132.
  47. Saeidi M, Mostafavi S, Heidari H & Masoudi S. Effects of a comprehensive cardiac rehabilitation program on quality of life in patients with coronary artery disease. ARYA Atheroscler 2013; 9: 179-185.
Health Management & Information Science
Volume 6, Issue 2 - Serial Number 2
39
April 2019
Pages 56-65

Files

Share

How to cite

Statistics